{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Born offset extended"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic stuff\n",
    "import sys\n",
    "sys.path.append(\"/net/server/homes/sep/gbarnier/code/gpu/acousticIsoOp/test/lib/python/\")\n",
    "import genericIO\n",
    "import SepVector\n",
    "import Hypercube\n",
    "import Acoustic_iso_double\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "# Plotting library\n",
    "import matplotlib.pyplot as plt\n",
    "import sepPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wavelet (sources)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "!/net/server/homes/sep/gbarnier/code/gpu/acousticIsoOp/test/bin/waveletMain.py timeDelay=1.0 f1=2 f2=5 f3=8 f4=15 par=parBorn.p type=ali wavelet=waveletBornExt.H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Velocity models (true and background)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "------------------------ Model padding program --------------------\n",
      "Original nz = 350 [samples]\n",
      "Original nx = 1700 [samples]\n",
      " \n",
      "zPadMinus = 100 [samples]\n",
      "zPadPlus = 110 [samples]\n",
      "xPadMinus = 100 [samples]\n",
      "xPadPlus = 104 [samples]\n",
      " \n",
      "blockSize = 16 [samples]\n",
      "FAT = 5 [samples]\n",
      " \n",
      "New nz = 570 [samples including padding and FAT]\n",
      "New nx = 1914 [samples including padding and FAT]\n",
      "-------------------------------------------------------------------\n",
      " \n",
      " \n",
      "------------------------ Model padding program --------------------\n",
      "Original nz = 350 [samples]\n",
      "Original nx = 1700 [samples]\n",
      " \n",
      "zPadMinus = 100 [samples]\n",
      "zPadPlus = 110 [samples]\n",
      "xPadMinus = 100 [samples]\n",
      "xPadPlus = 104 [samples]\n",
      " \n",
      "blockSize = 16 [samples]\n",
      "FAT = 5 [samples]\n",
      " \n",
      "New nz = 570 [samples including padding and FAT]\n",
      "New nx = 1914 [samples including padding and FAT]\n",
      "-------------------------------------------------------------------\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# True velocity model\n",
    "!/net/server/homes/sep/gbarnier/code/gpu/acousticIsoOp/test/bin/padFileGpuMain zPad=100 xPad=100 model=velocityMarmousi.H data=velocityMarmousi.pad.H\n",
    "\n",
    "# Background velocity model\n",
    "!Smooth rect1=10 rect2=10 < velocityMarmousi.H > velocityMarmousiSmooth.H\n",
    "!/net/server/homes/sep/gbarnier/code/gpu/acousticIsoOp/test/bin/padFileGpuMain zPad=100 xPad=100 model=velocityMarmousiSmooth.H data=velocityMarmousiSmooth.pad.H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model (extended reflectivity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \r\n",
      "------------------------ Model padding program --------------------\r\n",
      "Original nz = 350 [samples]\r\n",
      "Original nx = 1700 [samples]\r\n",
      " \r\n",
      "zPadMinus = 100 [samples]\r\n",
      "zPadPlus = 110 [samples]\r\n",
      "xPadMinus = 100 [samples]\r\n",
      "xPadPlus = 104 [samples]\r\n",
      " \r\n",
      "blockSize = 16 [samples]\r\n",
      "FAT = 5 [samples]\r\n",
      " \r\n",
      "New nz = 570 [samples including padding and FAT]\r\n",
      "New nx = 1914 [samples including padding and FAT]\r\n",
      "-------------------------------------------------------------------\r\n",
      " \r\n"
     ]
    }
   ],
   "source": [
    "# Create an extended reflectivity\n",
    "!Spike n1=350 n2=1700 d1=1.0 d2=1.0 n3=1 d3=1.0 velback=0.0 nsp=1 mag=0.01 k1=250 k2=850 > junk.H\n",
    "!Pad beg3=5 end3=15 extend=0 < junk.H > modelBornOffset.H\n",
    "!echo \"d1=0.01 d2=0.01 d3=0.01 o1=0.0 o2=0.0 o3=-0.1\" >> modelBornOffset.H\n",
    "\n",
    "# Pad extended reflectivity\n",
    "!/net/server/homes/sep/gbarnier/code/gpu/acousticIsoOp/test/bin/padFileGpuMain zPad=100 xPad=100 model=modelBornOffset.H data=modelBornOffset.pad.H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Born extended (offset) operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nts=1000\r\n",
      "dts=0.008\r\n",
      "sub=8\r\n",
      "nz=570\r\n",
      "nx=1914\r\n",
      "zPadMinus=100\r\n",
      "zPadPlus=110\r\n",
      "xPadMinus=100\r\n",
      "xPadPlus=104\r\n",
      "dz=0.01\r\n",
      "dx=0.01\r\n",
      "fMax=20\r\n",
      "zSource=2\r\n",
      "xSource=850\r\n",
      "nShot=1\r\n",
      "spacingShots=10\r\n",
      "depthReceiver=2\r\n",
      "nReceiver=1700\r\n",
      "dReceiver=1\r\n",
      "oReceiver=1\r\n",
      "blockSize=16\r\n",
      "fat=5\r\n",
      "nGpu=8\r\n"
     ]
    }
   ],
   "source": [
    "args=[\"dummy arg\",\"vel=velocityMarmousiSmooth.pad.H\",\"sources=waveletBornExt.H\",\"par=parBornExt.p\",\"model=modelBornOffset.pad.H\",\"extension=offset\",\"nExt=21\"]\n",
    "modelDouble,dataDouble,velDouble,parObject,sourcesVector,sourcesSignalsVector,receiversVector=Acoustic_iso_double.BornExtOpInitDouble(args)\n",
    "\n",
    "# Printing parameter file for reference\n",
    "!cat parBornExt.p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Born extended object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "BornOffsetOp=Acoustic_iso_double.BornExtShotsGpu(modelDouble,dataDouble,velDouble,parObject,sourcesVector,sourcesSignalsVector,receiversVector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Read model (extended reflectivity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelFile=parObject.getString(\"model\")\n",
    "modelFloat=genericIO.defaultIO.getVector(modelFile,ndims=3)\n",
    "modelDouble=SepVector.getSepVector(modelFloat.getHyper(),storage=\"dataDouble\")\n",
    "modelDMat=modelDouble.getNdArray()\n",
    "modelSMat=modelFloat.getNdArray()\n",
    "modelDMat[:]=modelSMat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Born offset extended forward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "BornOffsetOp.forward(False,modelDouble,dataDouble)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Born offset extended adjoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a model vector for adjoint result\n",
    "modelAdjDouble=SepVector.getSepVector(modelDouble.getHyper(),storage=\"dataDouble\")\n",
    "\n",
    "# Apply adjoint\n",
    "BornOffsetOp.adjoint(False,modelAdjDouble,dataDouble)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f90c00d8320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert data to 2D float \n",
    "dataFloat2D=SepVector.getSepVector(Hypercube.hypercube(axes=[dataDouble.getHyper().axes[0],dataDouble.getHyper().axes[1]]))\n",
    "dataFloat2DNd=dataFloat2D.getNdArray()\n",
    "dataDoubleNd=dataDouble.getNdArray()\n",
    "dataFloat2DNd[:]=dataDoubleNd\n",
    "\n",
    "# Plot forward\n",
    "sepPlot.Grey(plt,dataFloat2D,label1=\"Time [s]\",label2=\"Receivers [km]\",title=\"Forward\").output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'animation' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-92b04a6edcb6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Plot adjoint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msepPlot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGrey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodelAdjDouble\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"z [km]\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"x [km]\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Model\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/net/server/homes/sep/gbarnier/code/packages/pySepPlot/local/lib/python/sepPlot.py\u001b[0m in \u001b[0;36moutput\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    385\u001b[0m \t\t\t\t\t\tims.append(self.plt.imshow(mat,cmap=self.getColormap(),animated=True,\n\u001b[1;32m    386\u001b[0m \t\t\t\t\t\t\t\textent=self.getAxisLimits(),aspect=self.getParam(\"aspect\"),zorder=1))\n\u001b[0;32m--> 387\u001b[0;31m \t\t\tani = animation.ArtistAnimation(fig, ims, interval=50\n\u001b[0m\u001b[1;32m    388\u001b[0m                                         \u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m                                 repeat_delay=0)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'animation' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f90c0089978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot adjoint\n",
    "sepPlot.Grey(plt,modelAdjDouble,label1=\"z [km]\",label2=\"x [km]\",title=\"Model\").output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write results to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data \n",
    "dataFloat=SepVector.getSepVector(dataDouble.getHyper(),storage=\"dataFloat\")\n",
    "dataFloatNp=dataFloat.getNdArray()\n",
    "dataDoubleNp=dataDouble.getNdArray()\n",
    "dataFloatNp[:]=dataDoubleNp\n",
    "_=genericIO.defaultIO.writeVector(\"BornOffsetFwdJupyter.H\",modelFloat)\n",
    "\n",
    "# Model after applying FWD and ADJ\n",
    "modelFloat=SepVector.getSepVector(modelAdjDouble.getHyper(),storage=\"dataFloat\")\n",
    "modelFloatNp=modelAdjDouble.getNdArray()\n",
    "modelDoubleNp=modelAdjDouble.getNdArray()\n",
    "modelFloatNp[:]=modelDoubleNp\n",
    "_=genericIO.defaultIO.writeVector(\"BornOffsetAdjJupyter.H\",modelFloat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dot product test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dot-product test of forward and adjoint operators\n",
      "-------------------------------------------------\n",
      "Applying forward operator add=False\n",
      "\tRuns in: 4.510986566543579 seconds\n",
      "Applying adjoint operator add=False\n",
      "\tRuns in: 20.34583020210266 seconds\n",
      "Dot products add=False: domain=-3.935485122748255e-06 range=-3.935485122748026e-06 \n",
      "Absolute error: 2.295459287059154e-19\n",
      "Relative error: 5.83272256269211e-14 \n",
      "\n",
      "\n",
      "Applying forward operator add=True\n",
      "\tRuns in: 4.983800649642944 seconds\n",
      "Applying adjoint operator add=True\n",
      "\tRuns in: 20.878707885742188 seconds\n",
      "Dot products add=True: domain=-7.870970245496495e-06 range=-7.870970245495951e-06 \n",
      "Absolute error: 5.437951521372608e-19\n",
      "Relative error: 6.908870636989635e-14 \n",
      "\n",
      "-------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "BornOffsetOp.dotTest(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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